1. Field of the Invention
The present invention relates to the field of neuroscience for analyzing signals representative of a subject's brain activity including but not limited to signals indicative or predictive of epileptic seizures. More particularly, the invention concerns the automated analysis of brain activity signals to detect an activity state and transitions between states.
2. Brief Glossary of Terms and Useful Definitions
As used herein, certain terms and definitions are used as follows:
ECoG is the abbreviation for electrocorticogram which is a recording of voltage potentials obtained intracranially, e.g., directly from the cortex.
EEG is the abbreviation for electroencephalogram which is a recording of voltage potentials obtained from the scalp and encompasses any recordings outside the dura mater.
EKG is the abbreviation for an electrocardiogram.
EMG is the abbreviation for an electromyogram which is a recording of electrical muscle activity.
EOG is the abbreviation for an electrooculogram which is a recording of eye movements.
Epileptiform discharge and spike are used interchangeably herein to refer to a class of sharply contoured waveforms, usually of relatively large power, and with duration rarely exceeding two hundred milliseconds. Such spikes can form complexes with slow waves, and can occur in singlets, doublets, or in multiplets.
Epileptologist and electroencephalographer are used interchangeably.
False positive detection refers to the case of a system mistakenly detecting a non-seizure signal and classifying it as a seizure.
False negative detection describes the case in which a true seizure goes undetected by a system. Systems that have a low rate of false positive detections are called specific, while those with a low rate of false negative detections are called sensitive.
Ictal Period is the period of time during which a seizure is occurring. Those skilled in the art will appreciate that the term ictal can be applied to phenomena other than seizures.
Interictal Period is the period of time when the patient is not in the state of seizure, or in transition into or out of the seizure state.
Onset of the electrographic component of a seizure is defined by the appearance of a class of signal changes recognized by electroencephalographers as characteristic of a seizure. This analysis requires visual review of signal tracings of varying duration, both before and after the perceived signal changes, using multiple channels of information and clinical correlates. The precise determination of the onset is subject to personal interpretation, and may vary based on the skill and attention level of the reviewer, the quality of data and its display. Onset of the clinical component of a seizure is the earlier of either (1) the time at which the subject is aware that a seizure is beginning (the “aura”), or (2) the time at which an observer recognizes a significant physical or behavioral change typical of a seizure.
Postictal period corresponds to the time period between the end of a seizure and the beginning of the interictal period.
Preictal period corresponds to the time of transition between the interictal and the beginning of the ictal period.
Real-time describes a system with negligible latency between input and output.
State change: Any change in the behavioral, physical or chemical features/signals of a system or of a subject leading from the current to a different state. State changes may be normal or abnormal and endogenous, e.g., onset of sleep, or exogenous, e.g., administration of an anesthetic.
3. Description of the Related Art
Humans and animals have several normal states of behavior such as wakefulness and sleep, as well as multiple sub-states such as attentive wakefulness and REM sleep.
Disorders of the nervous system affect a large segment of the world population. Nervous system disorders include brain disorders that may be neurological or psychiatric, and disorders of the spinal cord, its roots, and peripheral nerves. Examples of such disorders include, but are not limited to, epilepsy, pain, migraine, Parkinson's disease, essential tremor, dystonia, multiple sclerosis (MS), anxiety, panic disorder, obsessive compulsive disorder, depression, bipolar illness, such as narcolepsy, sleep apnea, obesity, and anorexia.
Epilepsy, a disabling disease, affects 1-2% of the American and industrialized world's population, and up to 10% of people in under-developed countries. Electroencephalography is the single most important ancillary test in the investigation of this disease. EEG's are recorded continuously for hours to days in an increasing number of cases with unclear diagnosis or poor response to adequate medical treatment. The amount of EEG data for analysis is extremely large (e.g., sixty-four channels of data at 240 Hz yields 1.3 billion data points/24 hr or 2.6 Gigabytes/day) and consists of complex waveforms with infinite variations.
Visual analysis of these signals remains the “gold standard” but it is impracticable to conduct continuous EEG interpretation as this is the most time-consuming part of any electrodiagnostic test and requires special training and skills which make this procedure expensive and thus of limited access and use. Valuable EEG data is often discarded unexamined. The length of recording is unnecessarily prolonged in a specially equipped hospital suite until patients have several seizures. If the patient is unaware of the seizures, which is a common occurrence, then a nurse or relative must observe and document the presence of these occurrences. As seizures are brief and previously considered unpredictable, the need for continuous observation becomes imperative thereby adding to cost in an inefficient manner.
Present methods of seizure detection are not only expensive, but rely on poorly discriminating methods, increasing the review time and nursing assistance because of the large number of false positive detections, and increasing the length of hospitalization because of false negative detections. Furthermore, these methods often “detect” the seizure well after its onset, when prevention or abatement of the seizure is no longer possible or irrelevant.
The inability to process data in real time has thwarted scientific and clinical development in the fields of epilepsy and electroencephalography. Cardiology has developed into a clinical science largely based on the power of electrocardiography to analyze the heart's electrical activity in a rapid and accurate manner. This has resulted in pacemakers, implanted defibrillators, and other devices which have saved thousands of individuals from premature death. The comparison between cardiology/EKG and epilepsy/EEG must take into account the fact that electrical brain signals are far more complex than signals originating from the heart. This explains in large part the developmental lag between these two disciplines.
Electrical brain signals, because of their spatial and temporal characteristics such as non-stationarity, have resisted accurate real-time automatic manipulation. The prior art methods presently used to characterize these states are severely limited. For example, the prior art consists of a long history of failed attempts to identify changes in EEG during certain behavioral states or tasks and to discern epi-phenomenology from phenomenology, a distinction that would help answer questions of fundamental importance. Other limitations include the inability to determine whether signal spikes are a static marker of epilepsy, or whether they are dynamically related to seizure generation.
Most existing methods of automatic EEG analysis have major limitations which render them virtually useless for widespread, safe and effective clinical applications. These limitations include:
1) Lack of speed: the time it takes most methods to analyze input signals and produce an output which detects or predicts a state change is too lengthy for use in warning, intervention/blockage, or prevention of epileptic seizures and other abnormal brain states;
2) Limited accuracy: prior art methods produce a large number of false positive detections (incorrectly identifying non-seizure activity as a seizure) and false negative detections (failure to identify a true seizure), thereby increasing the technical and financial burden of such activities;
3) Limited adaptability to subject or seizure type;
4) Lack of portability and implantability; and
5) High cost.
Attempts to accurately and reproducibly predict behavioral or biologic signal changes associated with state changes such as seizures have been largely unsuccessful.
There have been, however, important advances in real-time seizure detection in the past fifteen years, most notably in the development of the method and system for seizure detection described in U.S. Pat. No. 5,995,868 to Osorio et al., which is incorporated herein by reference in its entirety. However, the prior art in seizure detection and real-time quantitative analysis of brain state does not utilize the additional, complementary information that can be obtained through determination of long-range dependencies in brain signals, such as those quantified by estimating the Hurst parameter of time series such as with an EEG or ECoG.